AWS Certified Machine Learning Engineer - Associate Exam

About this course

The AWS Certified Machine Learning Engineer – Associate (MLA-C01) certification validates your ability to build, deploy, and operate machine learning solutions on AWS. This course is your complete, exam-aligned guide to turning real-world ML work into production-ready AWS workflows—covering everything from data preparation and feature engineering to model development, deployment orchestration, and ongoing monitoring, maintenance, and security.Designed for learners who want practical, job-ready skills (not just theory), this course explains ML engineering concepts in a clear, structured way and shows how they map directly to AWS services and best practices. You’ll learn how to move beyond “training a model” and confidently manage the full lifecycle of an ML solution in a cloud environment.This course is fully aligned with the official AWS Certified Machine Learning Engineer – Associate (MLA-C01) exam blueprint and covers all four exam domains in depth:Domain 1: Data Preparation for Machine Learning (ML)Learn how to ingest, organize, and prepare data for ML workloads on AWS. You’ll cover core data preparation practices like cleaning and transforming data, handling missing values and outliers, selecting appropriate data formats, and creating reliable train/validation/test splits while avoiding data leakage. You’ll also learn how to build ML-ready datasets that support repeatable training and scalable production workflows.Domain 2: ML Model DevelopmentDevelop the skills needed to select the right modeling approach for a given problem, train models effectively, and evaluate results with the right metrics. You’ll learn practical techniques for feature engineering, model validation, handling overfitting/underfitting, and improving performance through tuning and experimentation. This domain emphasizes making engineering trade-offs—accuracy vs. cost, complexity vs. maintainability—so you can build models that perform